NASA Earth Exchange Global Daily Downscaled Projections Research Articles

Performance and projections of the NEX‐GDDP‐CMIP6 in simulating precipitation in the Brazilian Amazon and Cerrado biomes

AbstractThe objective of this work is to provide projections of mean annual and monthly precipitation for the Brazilian Amazon and Cerrado biomes, in the near‐term (2021–2040), medium‐term (2041–2060) and long‐term (2081–2100). The intermediate and most pessimistic Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emissions scenarios were considered. Thus, 34 high‐resolution global climate models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) Phase 6 of the Coupled Model Intercomparison Project (CMIP6) were evaluated. The base period evaluated was from 1981 to 2010. The NEX‐GDDP simulations are bias‐corrected and spatially disaggregated. The Climate Hazards Group InfraRed Precipitation with Station v2.0 was chosen as the source of observed data due to low availability in situ data. The Kling‐Gupta efficiency (KGE) and the global performance indicator were implemented in Google Earth Engine to evaluate the GCMs. The results show that the GCMs perform satisfactorily, except for KACE‐1‐0‐G and IITM‐ESM. The median KGE is 0.86 for the biomes. Thus, the Ensemble Model of 32 GCMs (EM‐32) indicates a reduction in precipitation in the biomes, except the northern Cerrado. In the most pessimistic scenario, changes in annual precipitation range from 3% to −33% until the end of the century. The north‐central Amazon and the northwestern Cerrado are the most affected regions. In general, the monthly precipitations between September and November show the most intense decreasing rates. It is estimated that 91% and 23% of areas in the Amazon and Cerrado biomes, respectively, show robust signs of reduction in mean annual precipitation. Thus, EM‐32 shows more intense and robust climate projections, in comparison to the total annual precipitation of the subset of 33 raw CMIP6 models from Working Group I of the IPCC Sixth Assessment Report. Therefore, the EM‐32 precipitation projections can be applied to future hydrological and hydrosedimentological investigations.

Improving simulations of extreme precipitation events in China by the CMIP6 global climate models through statistical downscaling

The recently released NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is a high-resolution daily downscaled dataset derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations. We comprehensively evaluated the performance of the NEX-GDDP dataset in simulating the characteristics of the climatological precipitation and extreme precipitation events across China from 1960 to 2014. We present here the projected changes in precipitation at the end of the 21st century (2070–2099) with respect to a reference period (1985–2014). We compared the NEX-GDDP dataset with both a state-of-the-art gauge data analysis system and the CMIP6 global climate models. The NEX-GDDP dataset significantly outperformed the CMIP6 simulations in replicating the climatological patterns of precipitation. It showed a closer alignment with the observed data, as evidenced by notably increased correlation coefficients and reduced model-relative errors. The NEX-GDDP dataset exceled in accurately reproducing the spatial distribution of indices of extreme precipitation, surpassing the performance of the CMIP6 simulations. The projections show an increased frequency of heavy precipitation under higher emission scenarios in both datasets, with the CMIP6 simulations consistently estimating a higher probability of heavy rain than the NEX-GDDP dataset.

Suitable areas for temperate fruit trees in a Brazilian hotspot area: Changes driven by new IPCC scenarios

Chilling accumulation and sufficient water availability are mandatory requirements for most temperate fruit trees (TFT) productivity. Given the strong dependence of these crops on air temperature and water availability, it is imperative to analyze the impacts of climate change on TFT, especially in areas considered persistent climate change hotspots. This study analyzed the impacts of climate change on suitable areas for planting TFT in a Brazilian state considered a hotspot for future climate changes - Minas Gerais, using an Agroclimatic Zoning (AZ) method, and outlined adaptation measures for the sustainable cultivation of the main TFT under future climate scenarios. Daily near-surface air temperature (minimum and maximum) and precipitation data were used from 10 Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), which are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6), in two Shared Socioeconomic Pathways scenarios (SSP2–4.5 and SSP5–8.5). The AZ method was applied for the current climate simulations (1995–2014), as well as for projections by the near (2041–2060) and far future (2081 – 2100) climate conditions considering two base temperature values (Tb = 7ºC and 13ºC). Given intense and persistent increases in air temperature, water deficit, and reduced chilling accumulation, TFT plantations will be restricted and economically unfeasible for about 69% (SSP2.4–5) to 86% (SSP5.8–5) of Minas Gerais in the far future for both Tb values. Only ∼6% (near future) and ∼2% (far future) of the study area located in a small portion of the Southern region of Minas Gerais will be suitable for TFT plantations. However, the future of TFT plantations in places classified as regular will depend on adopting adaptation measures to reduce vulnerability to these crops, including low-chill genotypes selection, improvements in breeding programs, using strategies to break dormancy artificially, and intensifying irrigation with less water-demanding techniques.

Future changes in the precipitation regime over the Arabian Peninsula with special emphasis on UAE: insights from NEX-GDDP CMIP6 model simulations

Global warming can profoundly influence the mean climate over the Arabian Peninsula, which may significantly influence both natural and human systems. The present study aims to investigate the changes in the precipitation regime in response to climate change over the Arabian Peninsula, with special emphasis on the United Arab Emirates (UAE). This work is performed using a sub-set of high-resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models under three different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The changes are analyzed in three phases such as 2021–2050 (near future), 2051–2080 (mid future) and 2080–2100 (far future), with the period of 1985–2014 as the baseline. This study represents the first attempt to utilize data from NEX-GDDP models to project the regional patterns of precipitation regime across the Arabian Peninsula. Results suggest that the annual precipitation is expected to increase over most of the UAE by up to 30%, particularly intense from the mid-future onwards in all scenarios. Specifically, the spatiotemporal distribution of precipitation extremes such as intensity, 1-day highest precipitation, and precipitation exceeding 10 mm days are increasing; in contrast, the consecutive dry days may decrease towards the end of the century. The results show that the changes in extreme precipitation under a warming scenario relative to the historical period indicate progressive wetting across UAE, accompanied by increased heavy precipitation events and reduced dry spell events, particularly under the high emission scenarios. A high-resolution dataset is essential for a better understanding of changes in precipitation patterns, especially in regions where more detailed information is needed on a local scale to achieve water, food security, and environmental sustainability to formulate effective adaptation strategies for mitigating the potential risks and consequences associated with variations in wet and dry conditions.

Assessing hydrological response to future climate change in the Bouregreg watershed, Morocco

Hydrological systems in watersheds encounter growing challenges attributed to climate change, marked by shifts in precipitation patterns, heightened drought severity, and altered runoff dynamics. This study explores the implications of these changes for streamflow in the vulnerable Bouregreg catchment.This study used a novel approach in the Bouregreg watershed to address this issue by applying the underutilized GR2M model, deviating from the prevalence of ArcSWAT and other models in previous studies. It incorporates bias-corrected outputs from the CNRM-CM5 climate model, sourced from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The objective was to project monthly streamflow for the 2040s (2030-2050) and compare it with the reference period (1982-2001), considering the Representative Concentration Pathways (RCPs): medium RCP4.5 and high RCP8.5. The methodology started with predicting future precipitation and temperatures using the regional climate model. Then, the future average rainfall for each sub-basin was determined using the Thiessen method, and the future potential evapotranspiration (PET) was estimated through the Thornthwaite formula, considering the model-predicted temperatures. Following this, the hydrological model was calibrated and validated, leading to streamflow projection for the 2040s period under the two emission scenarios.The choice to utilize climate models and hydrological modeling techniques was driven by the need to provide valuable insights for water resource management. By projecting future streamflow under different emission scenarios, the study aimed to inform decision-makers about the potential impacts of climate change on water resources.The study's findings revealed that significant climatic changes would occur in the Bouregreg catchment under the RCPs. Projections indicated a noticeable increase in mean temperatures, with expected rises of about 1.32°C for RCP4.5 and 1.69°C for RCP8.5. Additionally, the PET is foreseen to rise by 5.38 mm under RCP4.5 and 6.27 mm under RCP8.5, while precipitation levels are anticipated to experience significant reductions, decreasing by 33.74% for RCP4.5 and 40.20% for RCP8.5. These pronounced climate alterations are projected to lead to an annual decrease in streamflow. For RCP4.5, this reduction is estimated at 44.63%, and for RCP8.5, it is even more significant at 64.30%. The alterations projected for the 2040s signify a substantial departure from the conditions observed during the reference period of 1982-2001.The importance of these findings lies in their utility for decision-makers in developing effective strategies and facilitating the implementation of adaptation measures for managing water resources and ensuring their sustainable use amid evolving climate conditions.

Meteorological and Hydrological Drought Risks under Future Climate and Land-Use-Change Scenarios in the Yellow River Basin

The primary innovation of this study lies in the development of an integrated modeling framework that combines downscaled climate projections, land-use-change simulations, and copula-based risk analysis. This framework allows for the assessment of localized sub-seasonal and seasonal drought hazards under future scenarios. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent the future climate for 2025–2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future land-use-change distributions. The climate–land use–drought modeling nexus enables the generation of refined spatio-temporal projections of meteorological and hydrological drought risks in the Yellow River Basin (YRB) in the future period of 2025–2060. The results highlight the increased vulnerability of the upper YRB to sub-seasonal meteorological droughts, as well as the heightened sub-seasonal hydrological drought risks in the Loess Plateau. Furthermore, downstream areas experience escalated seasonal hydrological drought exposure due to urbanization. By providing actionable insights into localized future drought patterns, this integrated assessment approach advances preparedness and climate adaptation strategies. The findings of the study enhance our understanding of potential changes in this integral system under the combined pressures of global climate change and land use shifts.

Climate risks and vulnerabilities of the Arabica coffee in Brazil under current and future climates considering new CMIP6 models

The susceptibility to climate change concerns the coffee market worldwide due to possible severe productivity losses. Brazil is the world's largest Arabica coffee producer and has crops in regions considered persistent climate change hotspots. Our study analyzed risks, vulnerabilities, and susceptibilities to pests and diseases in these regions under current and future climates and outlined adaptive measures to reduce future vulnerabilities. Ten risk indicators based on Arabica coffee requirements were proposed: water supply (Iw), base (TIB) and maximum temperature stresses (TImax), which delimit the temperature range where Arabica coffee grows and productivity is penalized outside both ranges, frost stress (TIfrost), diseases such as rust (DIrust), brown eye spot (DIbrown), and Phoma leaf spot (DIphoma), pests such as coffee berry borer (PIberry), coffee leaf miner (PIminer), and yield loss due to water stress (Iyg). Daily near-surface air temperature (minimum, mean, and maximum), relative humidity, precipitation, and global solar radiation were used from 16 General Circulation Models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), which are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in three Shared Socioeconomic Pathways scenarios (SSP245, SSP370 and SSP585). All risk indicators were calculated for the current climate (1995–2014) and projected for the near (2041–2060), intermediate (2061–2080), and far future (2081–2100) in three SSPs and then classified into five risk classes (very low, low, moderate, high and very high). Our results indicated that due to increases in TImax and Iyg indicators, with high to very high risk in area and magnitude, Arabica coffee plantations will be negatively affected and economically unfeasible for about 35 % to 75 % of the studied area throughout the 21st century. Furthermore, the rust and the leaf miner will remain a concern in future climates due to increased temperatures and reduced relative humidity. The future of Arabica coffee crops in Brazil will depend on adopting effective adaptive measures and prudent agricultural strategies to address anticipated risks, including shifting crops to higher altitude areas, introducing more climate-resilient coffee cultivars/varieties, using agroforestry or intercropping systems, planting in closer spacing or higher density planting, and employing dripper or partial root-zone irrigation techniques.

Valuing mortality attributable to present and future temperature extremes in Argentina

This study analyzes the weather-related health damage of present and future extreme temperatures in Argentina. Focusing on mortality, short-term impacts of temperature are obtained by regressing monthly mortality rates on inter-annual monthly weather variability. For this purpose, a countrywide panel dataset at the municipal level was constructed from the universe of deaths between 2010 and 2019, and daily meteorological records from the ERA5 weather dataset. Then, NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) are used to project future mortality by 2085 under two climate scenarios. Finally, present and future mortality-related economic damages are assessed using the Value of a Statistical Life. The results show that one additional day of extreme temperatures increase all-cause mortality rates relative to mild weather and that the impact of hotter-than-average temperatures is greater in magnitude than that of colder ones. Substantial heterogeneity exists between causes of death and age groups, with older people facing greater risks, while the results for gender are inconclusive. All days of extreme cold in a year generate damage equivalent to 0.64% of GDP, while heat damage is 0.11% of GDP. The total damage by extreme temperatures adds up to 0.75% of the 2019 GDP. When future temperatures are valued, the total damage increases by an additional 1.45% under scenario RCP8.5 because the lower mortality occurring on cold days only partially offsets the increase in the number of hot days. On the contrary, if temperature changes were to be mild (i.e., under scenario RCP4.5), overall mortality would be lower at the national level and the corresponding damages would decrease by 0.02%.

What Does Global Land Climate Look Like at 2°C Warming?

AbstractConstraining an increase in global mean temperature below 2°C compared to pre‐industrial levels is critical to limiting dangerous and cascading impacts of anthropogenic climate change. Understanding future climatic changes and their spatial heterogeneity at 2°C warming is thus important for policy makers to prepare actionable adaptation and mitigation plans by identifying where and to what extent lives and livelihoods will be impacted. This study uses the recently released NASA Earth eXchange Global Daily Downscaled Projections (NEX‐GDDP) CMIP6 data to provide a broad overview of projected changes in six key climate variables and two climate impact indicators at a time when warming exceeds 2°C. Analysis of global mean temperature changes indicates the 2040s as the decade when most CMIP6 models reach 2°C warming with respect to a pre‐industrial period (1850–1900). During the 2040s, we find that global mean temperature, precipitation, relative humidity, downwelling shortwave and longwave radiation, and wind speed over land under the high emission scenario are projected to change by +2.8°C, +22.4 mm/year, −0.73%, −2.23 , +15.9 W/m2, and −0.04 m/s, respectively. Many of the future changes are expected to exacerbate climate impacts including heat stress and fire danger. Our analysis shows geographic patterns of policy‐relevant climatic changes, as parts of the globe will experience significant climate impacts even if the goal to keep warming below 2°C goal is achieved. Our results highlight the urgent need for further studies focused on identifying key hotspots and advancing region‐specific actionable adaptation and mitigation plans.

Predicting seedling development for two commercial forest species under current and future climates: A multi-model assessment

Development models are tools that couple environmental effects on development, allowing to predict development dynamics and to quantify the seedling phase duration for forest species under current and future climate conditions. Unfortunately, studies on forest species are scarce, even for commercially important species. This study calibrates and evaluates two development models under current climate conditions, and identifies possible changes in future climate conditions on the development of Corymbia citriodora (Hook.) (K.D. Hill & L.A.S. Johnson) and Eucalyptus urophylla (S.T. Blake), which are widely used in Brazilian forest plantations. The Phyllochron (Phyl) and Wang and Engel (WE) development models estimate the daily cumulative leaf number (CLN), and when integrated over time, gives the seedling phase duration (SPD). Field experiments were conducted using eleven sowing dates (SD) during 2014 and 2015 growing seasons in Itajubá, Minas Gerais, Brazil to evaluate the performance and calibrate the model coefficients for predicting the development of the two species. Subsequently, the best model was simulated for current climate conditions (1980–2005) and projected for future climates (2021–2050 and 2071–2100) under two radiative forcing scenarios (RCP 4.5 and 8.5) considering eleven SDs. Data from nine Earth System Models from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), that are derived from Coupled Model Intercomparison Project Phase 5 (CMIP5) were used. The Phyl and WE models were capable of predicting the CLN and SPD developmental dynamics for both species. However, the cumulative errors in CLN from the Phyl model, generated less accurate estimates for SPD. By contrast, the WE model was excellent (superior to the Phyl) at estimating CLN and SPD, with an error of less than 2.6 leaves, and 6 days, respectively. Additionally, the projected increases of up to 4 °C throughout the 21st century may modify the development rates, increase the SPD (∼+ 15 days), thus interfering with the permanence time of the two species at forest nursery, the date of sale, and seedling quality. Since the climate change time ranges exert greater influence on the SPD of both species, changing the sowing date and artificially controlling seedling production environments are the most viable adaptation measures to cope future climate change threats.

Tracing seismic landslide-derived sediment dynamics in response to climate change

Mass movements such as landslides are prominent sources of sediment in post-seismic mountain belts. Earthquake triggered landslides can generate substantial volumes of fresh sediment and this may have significant long-term (decade to millennium) geomorphic effects on landscape evolution. Downslope and downstream transport of sediment into the channel network is sensitive to meteorological perturbations especially for extreme rainfall events. However, no established techniques can explicitly fingerprint/track landslide-derived sediment under climate change. In this study, we use a new tracing function incorporated into CAESAR-Lisflood (CL) to track the landslide-derived sediment dynamics in response to climate change. The hourly future rainfall sequences under two climate scenarios (RCP4.5, RCP8.5) were firstly generated using ‘NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)’ dataset and then used as hydrological input to drive CL simulations. The landscape evolution at the Hongxi basin, which suffered great damage from the Wenchuan earthquake (Ms 8.0), was then simulated for 93 years (2008–2100) using CL under two climate scenarios. The results show that the landslides can greatly increase the sediment yield and climatic perturbations can lead to variation of sediment dynamics, the coupling of these two factors perform a combined impact on sediment budget, while the interplay and relevant importance between landslides and climate impact is dependent on the landslide density of specific basin. The landslide-channel connectivity (location of landslides with respect to river channels) plays an important role in post-earthquake sediment evacuation. The yearly sediment yield generated by the landslides can be stable after 20 years under both RCP 4.5 and RCP 8.5 scenarios in the study basin. These findings are of great importance to understand the interplay between landslide and climatic perturbations and support the risk management and mitigation after the 2008 Wenchuan earthquake.

Physically-based distributed modelling of the hydrology and soil erosion under changes in landuse and climate of a humid tropical river basin

The effects of landuse/landcover (LULC) and climate change on hydrology and soil erosion processes are of major concern, especially in the humid tropics. In this study, an evaluation of these changes is performed in a humid tropical catchment (Vamanapuram river basin, India) using a physically based distributed model, SHETRAN. The past landuse maps and climate data from six fine resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) are used to force the SHETRAN model. A comparison of the change in the rate of soil erosion and hydrological responses during the future climate scenarios (near: 2021–2050 and far: 2071–2100) with respect to the historical period (1980–2005) is conducted.The isolated effects of landuse, climate variability and the combined effect caused change in streamflow (1.1%, 7.9%, 9.0% respectively) and sediment load (−10.4%, 1.7%, −10.5% respectively) in the past. An ensemble mean of general circulation model (GCM) projections showed that mean temperature and average annual precipitation under representative concentration pathway RCP 4.5 (8.5) scenarios for near and far futures increased from the historic period by 0.51 °C (1.6 °C), 0.6 °C (3.2 °C) and by 9% (6%), 15% (22.8%), respectively. Under RCP4.5(8.5), the average annual streamflow increased gradually from the near to far future, by 3.2% (−1.8%) and 13.6 % (22.1%) whereas, the projected sediment load a showed change of −21.05% (−26.63%) in near future while far future indicated change by −11% (4%). The SHETRAN model has been found to be effective in evaluating climate change impacts on hydrology and sediment yield and is useful for future river basin management.

Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)

Lakes are major surface water resource in semi-arid regions, providing water for agriculture and domestic use. Prediction of future water availability in lakes of semi-arid regions is important as they are highly sensitive to climate variability. This study is to examine the water level fluctuations in Pakhal Lake, Telangana, India using a combination of a process-based hydrological model and machine learning technique under climate change scenarios. Pakhal is an artificial lake built to meet the irrigation requirements of the region. Predictions of lake level can help with effective planning and management of water resources. In this study, an integrated approach is adopted to predict future water level fluctuations in Pakhal Lake in response to potential climate change. This study makes use of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset which contains 21 Global Climate Models (GCMs) at a resolution of 0.25 × 0.25° is used for the study. The Reliability Ensemble Averaging (REA) method is applied to the 21 models to create an ensemble model. The hydrological model outputs from Soil and Water Assessment Tool (SWAT) are used to develop the machine-learning based Support Vector Regression (ν-SVR) model for predicting future water levels in Pakhal Lake. The scores of the three metrics, correlation coefficient (R2), RMSE and MEA are 0.79, 0.018 m, and 0.13 m, respectively for the training period. The values for the validation periods are 0.72, 0.6, and 0.25 m, indicating that the model captures the observed lake water level trends satisfactorily. The SWAT simulation results showed a decrease in surface runoff in the Representative Concentration Pathways (RCP) 4.5 scenario and an increase in the RCP 8.5 scenario. Further, the results from ν-SVR model for the future time period indicate a decrease in future lake levels during crop growth seasons. This study aids in planning of necessary water management options for Pakhal Lake under climate change scenarios. With limited observed datasets, this study can be easily extended to the other lake systems.

Accelerated exacerbation of global extreme heatwaves under warming scenarios

AbstractIt is generally believed that global warming drives an increase in heatwaves, but these changes vary regionally. Projected trends of heatwaves and comparisons between observed and projected heatwave trends are poorly understood. We selected multiple characteristics of global heatwave events, including indicators on heat‐related health impacts under historical and future scenarios from the NASA Earth Exchange/Global Daily Downscaled Projections (NEX‐GDDP) dataset. We quantified the trends in the frequency, intensity, duration and peak temperature of heatwave events and identified heatwave hotspots that respond dramatically to radiative forcing. Future simulations suggest a four‐fold increase in the duration of heatwaves by 2050s, spatially concentrated in central Africa, northern South America and Southeast Asia, and the maximum duration of single heatwave event will be up to 44 days under a high emission scenario. Accelerated increasing trends are also detected in intensity, total duration and temperature of heatwaves with up to 2‐fold, 8‐fold and 9‐fold larger than the trends of the baseline period under the high emission scenario. Considering socioeconomic exposure to extreme heatwaves, we identified some hotspot areas in western Europe, eastern North America and northern China that will face greater potential risks in the coming future and therefore need to urgently strengthen their adaptation capacity.

Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

For effective management practices and decision-making, the uncertainty associated with Regional Climate Models (RCMs) and their scenarios need to be assessed in the context of climate change. The present study analyzes the various uncertainties in the precipitation and temperature datasets of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under Representative Concentrative Pathways (RCPs) 4.5 and 8.5 over the Munneru river basin, in India, using the Reliable Ensemble Averaging (REA) method. From the available 21 RCMs, the top five ranked are ensembled and bias-corrected at each grid using the non-parametric quantile mapping method for the precipitation and temperature datasets. The spatio-temporal variations in precipitation and temperature data for the future periods, i.e., 2021–2039 (near future), 2040–2069 (mid future) and 2070–2099 (far future) are analyzed. For the period 2021–2099, annual average precipitation increases by 233 mm and 287 mm, respectively, the in RCP 4.5 and RCP 8.5 scenarios when compared to the observed period (1951–2005). In both the RCP 4.5 and RCP 8.5 scenarios, the annual average maximum temperature rises by 1.8 °C and 1.9 °C, respectively. Similarly, the annual average minimum temperature rises by 1.8 °C and 2.5 °C for the RCP 4.5 and RCP 8.5 scenarios, respectively. The spatio-temporal climatic variations for future periods obtained from high-resolution climate model data aid in the preparation of water resource planning and management options in the study basin under the changing climate. The methodology developed in this study can be applied to any other basin to analyze the climatic variables suitable for climate change impact studies that require a finer scale, but the biases present in the historical simulations can be attributed to uncertainties in the estimation of climatic variable projections. The findings of the study indicate that NEX-GDDP datasets are in good agreement with IMD datasets on monthly scales but not on daily scales over the observed period, implying that these data should be scrutinized more closely on daily scales, especially when utilized in impact studies.

Future projections of temperature and precipitation for Antarctica

Antarctica directly impacts the lives of more than half of the world’s population living in the coastal regions. Therefore it is highly desirable to project its climate for the future. But it is a region where the climate models have large inter-modal variability and hence it raises questions about the robustness of the projections available. Therefore, we have examined 87 global models from three modelling consortia (Coupled Model Intercomparison Project Phase 5 (CMIP5), Coupled Model Intercomparison Project Phase 6 (CMIP6), and NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)), characterized their fidelity, selected a set of ten models (MM10) performing satisfactorily, and used them to make the future projection of precipitation and temperature, and assessed the contribution of precipitation towards sea-levels. For the historical period, the multi-model mean (MMM) of CMIP5 performed slightly better than CMIP6 and was worse for NEX-GDDP, with negligible surface temperature bias of approximately 0.5 °C and a 17.5% and 19% biases in the mean precipitation noted in both CMIP consortia. These biases considerably reduced in MM10, with 21st century projections showing surface warming of approximately 5.1 °C–5.3 °C and precipitation increase approximately 44%–50% against ERA-5 under high-emission scenarios in both CMIP consortia. This projected precipitation increase is much less than that projected using MMM in previous studies with almost the same level of warming, implying approximately 40.0 mm yr−1 contribution of precipitation towards sea-level mitigation against approximately 65.0 mm yr−1.

A model for predicting the initial development of two native forest species under current and future climates

Development models are tools used to evaluate the development of forest species under current and future climate conditions. However, there are scarce studies on native forest species, despite their ecological and economic potential. This study aims to calibrate and evaluate two development models under field experimental conditions, as well as to identify possible changes in the development of the Anadenanthera peregrina (L.) Speg. and Libidibia ferrea (Mart. ex Tul.) L.P.Queiroz, both forest species native to Brazil, under current and future climate conditions. The Phyllochron (Phyl) and Wang and Engel (WE) development models estimate on a daily basis the cumulative leaf number (CLN), which integrated on time provides the seedling phase duration (SPD). First, both models were parameterized and evaluated under field experimental conditions. For that, data from field experiments conducted during 2017 and 2018 growing seasons and twelve sowing dates in Itajubá, Minas Gerais, Brazil, were used. These two species X sowing dates x years experiments provided a rich data set for parameterizing and evaluating the Phyl and WE development models. Subsequently, the best development model was applied on current (1980–2004) and on two future (2021–2050 and 2071–2099) climate conditions in two emission scenarios (RCP4.5 and RCP8.5). Data from eight Earth System Models from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) were used. Both development models were able to predict the CLN for both species, with less accuracy in SPD prediction. For A. peregrina the Phyl model was superior to the CLN (RMSE = 2.93 leaves), however, it was not accurate in predicting the SPD (RMSE = 29.1 days). For L. ferrea the WE model was superior mainly in SPD prediction (RMSE = 9.2 days). Climate projections indicate an increase in air temperature (up to 3.9 °C in 2100) and changes in the initial development of the two native forest species. While to change sowing dates is a feasible adaptation measure against the threats of climate change, shading and irrigation may reduce the quality of seedlings. Outlining appropriate, timely and cost effective adaptation measures is critical for the sustainability of the Brazilian nursery sector.

Future precipitation, hydrology and hydropower generation in the Yalong River Basin: Projections and analysis

In recent years, the impact of global warming has raised public concern over the precipitation-induced geological hazards, water security and energy security in the Yalong River Basin (YRB). Therefore, it is advisable to identify the variations of future precipitation, hydrology and hydropower generation under the joint impact of global warming and the operation of local hydropower reservoirs. To this end, the ensemble mean of the bias corrected NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) climate projections downscaled from 10 global climate models (GCMs) was applied to seven precipitation indices proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI) and the ensemble of two distributed hydrologic models (VIC and CREST) under the global warming of 1.5 °C and 2 °C. Reservoir operation based on actual operation rules and the hydropower optimization model were explicitly included in the routing module to represent the impact of the local hydropower reservoirs, respectively. The future natural runoff is likely to increase under RCP8.5, decrease under RCP4.5, and see a larger daily variability under both. The local hydropower reservoirs can effectively mitigate the negative climate impact on the basin hydrology, and the basin is expected to see increased dry-season runoff, decreased wet-season runoff, and lower daily variability of runoff under all scenarios as compared to the baseline period. The future hydropower generation of the YRB is consistent with the variation of future runoff, and reservoir operation with hydropower optimized rules can produce about 5% electricity more than the current actual operation rules, highlighting the potential need to adapt the current operation rules to future climate conditions. Our framework and findings are expected to provide instructive information for water resources management not only in the YRB but also in different regions worldwide.

Projections of temperature extremes based on preferred CMIP5 models: a case study in the Kaidu-Kongqi River basin in Northwest China

The extreme temperature has more outstanding impact on ecology and water resources in arid regions than the average temperature. Using the downscaled daily temperature data from 21 Coupled Model Inter-comparison Project (CMIP) models of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) and the observation data, this paper analyzed the changes in temporal and spatiotemporal variation of temperature extremes, i.e., the maximum temperature (Tmax) and minimum temperature (Tmin), in the Kaidu-Kongqi River basin over the period 2020–2050 based on the evaluation of preferred Multi-Model Ensemble (MME). Results showed that the Partial Least Square ensemble mean participated by Preferred Models (PM-PLS) was better representing the temporal change and spatial distribution of temperature extremes during 1961–2005 and was chosen to project the future change. In 2020–2050, the increasing rate of Tmax (Tmin) under RCP (Representative Concentration Pathway) 8.5 will be 2.0 (1.6) times that under RCP4.5, and that of Tmin will be larger than that of Tmax under each corresponding RCP. Tmin will keep contributing more to global warming than Tmax. The spatial distribution characteristics of Tmax and Tmin under the two RCPs will overall the same; but compared to the baseline period (1986–2005), the increments of Tmax and Tmin in plain area will be larger than those in mountainous area. With the emission concentration increased, however, the response of Tmax in mountainous area will be more sensitive than that in plain area, and that of Tmin will be equivalently sensitive in mountainous area and plain area. The impacts induced by Tmin will be universal and far-reaching. Results of spatiotemporal variation of temperature extremes indicate that large increases in the magnitude of warming in the basin may occur in the future. The projections can provide the scientific basis for water and land plan management and disaster prevention and mitigation in the inland river basin.

High‐resolution climate change projection of northeast monsoon rainfall over peninsular India

AbstractIn this study, projected changes in mean northeast monsoon (NEM) rainfall and associated extreme rainfall and temperature events, over peninsular India (PI) and its six subdivisions, are quantified. High‐resolution dynamically downscaled simulations of the Weather Research and Forecasting (WRF) regional climate model driven by the boundary conditions from the Community Climate System Model version 4 (CCSM4) model (WRF‐CCSM4) are compared with statistically downscaled simulations of NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP). Over PI, these downscaled simulations show low bias in mean NEM rainfall (≤ − 0.44 mm·day−1) and high pattern correlation coefficient (≥0.75), giving confidence in their future projections. Under future warming over PI, both downscaled simulations project future significant enhancement in NEM rainfall with WRF‐CCSM4 projecting 1.98 mm·day−1 (83.78% change with respect to the present‐day mean) whereas the multimodel ensemble (MME) of eight NEX‐GDDP models project 0.67 ± 0.58 mm·day−1 (19.78%) by the midddle of the century and 1.42 ± 0.97 mm·day−1 (42.76%) by the end of the century. Analysis of extreme rainfall events shows that WRF‐CCSM4 projects future enhancement (reduction) in extreme rainfall (R95p) days over 91.4% (8.6%) of grid‐points over PI. In future, coastal areas of Karnataka and Andhra Pradesh will likely experience increased extreme rainfall occurrence by more than 25 days and 15–20 days respectively. Projected future enhancement in the mean and extreme NEM rainfall is attributed to the increased precipitable water under a warming climate. Future projection of extreme temperature indices shows an increase in minimum and maximum temperatures over PI during the NEM season. Over PI, future winter nights and days are found to be warmer than those in the present day and the temperature change in future winter nights is found to be larger than that in winter days. This climate change information would help decision‐makers in evaluating existing policies and devising revised policies to reduce risk due to climate change.